Grayscale Thermographic Imaging

ABSTRACT

Through the measurement and interpretation of the pixels of grayscale digital thermographic images of abnormalities of the skin and its subcutaneous tissue, early intervention and treatment of abnormalities of the skin and its subcutaneous tissue are possible, thereby assisting clinicians in making significant impacts on prevention and treatment.

CROSS REFERENCE TO RELATED APPLICATIONS

The present divisional application claims priority to Non-Provisionalpatent application Ser. No. 13/439,177, filed Apr. 4, 2012.

BACKGROUND

Over the last century, clinicians, which term includes herein certifiedand licensed medical doctors of all specialties, osteopathic doctors ofall specialties, podiatrists, dental doctors of all specialties,chiropractors, veterinarians of all specialties, nurses, and medicalimaging technicians, have become dependent on the use of medical devicesthat assist them in their delivery of patient-centered care. The commonfunction of these devices is to assist and not replace the clinicaljudgment of the clinician. This fulfills the dictum that best practiceis clinical judgment assisted by scientific data and information.

Entering into the era of computer science and sophisticated electronics,clinicians have the opportunity to be supported by data and informationin a statistically significant and timely fashion. These advancementshave allowed more extensive and useful collection of meaningful datathat can be acquired, analyzed, and applied in conjunction with theknowledge and expertise of the clinician.

Medical long-wave infrared (LIR) thermography has been known to bebeneficial in the evaluation of thermal heat intensity and gradiencyrelating to abnormalities of the skin and subcutaneous tissue (SST).Although this technology has expanded to other areas of medicalevaluation, the scope of this patent application is limited to the SSTabnormalities. These abnormalities include the formation of deep tissueinjury (DTI) and subsequent necrosis caused by mechanical stress,infection, auto-immune condition, and vascular flow problems. DTI causedby mechanical stress (pressure, shear and frictional forces) can beseparated into three categories. The first category is a highmagnitude/short duration mechanical stress represented by traumatic andsurgical wounds. The second category is low magnitude/long durationmechanical stress represented by pressure ulcer development, which isalso a factor in the development of ischemic and neuropathic wounds. Thethird category is a combination of categories one and two represented bypressure ulcer formation in the bariatric patient.

The pathophysiologic conditions that occur with DTI and subsequentnecrosis of the affected tissue are ischemia, cell distortion, impairedlymphatic drainage, impaired interstitial fluid flow, and reperfusioninjury: Category one is dominated by cell distortion and evendestruction. Category two is dominated by ischemia. Category three is acombination of cell distortion and ischemia.

Hypoxia causes aerobic metabolism to convert to anaerobic metabolism.This occurrence causes lactic acidosis followed by cell destruction,release of enzymes and lytic reactions. The release of these substancescauses additional cell injury and destruction, and initiation of theinflammatory response.

It is very important to recognize that ischemic-reperfusion injury isassociated with all of the above mechanical stress induced SST injuries.This condition is caused by a hypoxia induced enzymatic change and therespiratory burst associated with phagocytosis when oxygen returns afteran ischemic event. The result of ischemic-reperfusion injury is theformation of oxygen free radicals (hydroxyl, superoxide, and hydrogenperoxide) that cause damage to healthy and already injured cells leadingto extension of the original injury.

SST injury and subsequent necrosis can also be caused by vasculardisorders. Hypoxia can be caused by an arterial occlusion or by venoushypertension. Lymphatic flow or node obstruction can also createvascular induced injury by creating fibrous restriction to venousdrainage and subsequent cellular stasis in the capillary system. Thesedisorders are also accentuated by reperfusion injury and oxygen freeradical formation.

Infection of the skin (impetigo), subcutaneous tissue (cellulitis), deeptissue (fasciitis), bone (osteomyelitis) and cartilage (chondritis)causes injury and necrosis of the affected tissue. Cells can be injuredor destroyed by the microorganism directly, by toxins released by themicroorganism and/or the subsequent immune and inflammatory response.These disorders arc also accentuated by reperfusion injury and oxygenfree radical formation.

Auto-immune morbidities of the skeletal joints (rheumatoid arthritis),subcutaneous tissue (tendonitis, myelitis, dermatitis) and blood vessels(vasculitis) cause similar dysfunction and necrosis of the tissue beingaffected by the hypersensitivity reactions on the targeted cells and thesubsequent inflammatory response. Again, these conditions areaccentuated by reperfusion and oxygen free radical formation.

The common event that addresses all of the above SST injuries is theinflammatory response. This response has two stages. The first stage isvascular and the second is cellular. The initial vascular response isvasoconstriction that will last a short time. The constriction causesdecrease blood flow to the area of injury. The decrease in blood flowcauses vascular “pooling” of blood (passive congestion) in the proximalarterial vasculature in the region of injury and intravascular cellularstasis occurs along with coagulation.

The second vascular response is extensive vasodilation of the bloodvessels in the area of necrosis. This dilation along with the “pooled”proximal blood causes increased blood flow with high perfusion pressureinto the area of injury. This high pressure flow can cause damage toendothelial cells. Leakage of plasma, protein, and intravascular cellscauses more cellular stasis in the capillaries (micro-thrombotic event)and hemorrhage into the area of injury. When the perivascular collagenis injured, intravascular and extravascular coagulation occurs. Therupture of the mast cells causes release of histamine that increases thevascular dilation and the size of the junctions between the endothelialcells. This is the beginning of the cellular phase. More serum and cells(mainly neutrophils) enter into the area of the mixture of injured anddestroyed cells by the mechanism of marginalization, emigration(diapedesis) and the chemotaxic recruitment (chemotaxic gradiency).Stalling of the inflammatory stage can cause the area of necrosis (ringof ischemia) to remain in the inflammatory stage long past theanticipated time of 2-4 days. This continuation of the inflammatorystage leads to delayed resolution of the ischemic necrotic event.

The proliferation stage starts before the inflammatory stage recedes. Inthis stage angiogenesis occurs along with formation of granulation andcollagen deposition. Contraction occurs, and peaks, at 5-15 days postinjury.

Re-epithelialization occurs by various processes depending on the depthof injury. Partial thickness wounds can resurface within a few days.Full thickness wounds need granulation tissue to form the base forre-epithelialization to occur. The full thickness wound does not heal byregeneration due to the need for scar tissue to repair the wound. Therepaired scarred wound has less vascularity and tensile strength ofnormal regional uninjured SST. The final stage is remodeling. In thisstage the collagen changes from type III to a stronger type I and isrearranged into an organized tissue.

All stages of wound healing require adequate vascularization to preventischemia, deliver nutrients, and remove metabolic waste. Following thevascular flow and metabolic activity of a necrotic area is currentlymonitored by patient assessment and clinical findings of swelling, pain,redness, increased temperature, and loss of function.

SUMMARY

Having a real time control allows an area of interest (AOI) to berecognized. The AOI can be of greater intensity (hotter) or lessintensity (cooler) than the normal SST of that region of the body. TheAOI can then be evaluated by the clinician for the degree of metabolism,blood flow, necrosis, inflammation and the presence of infection bycomparing the warmer or cooler thermal intensity of the AOI or woundbase and peri-AOI or wound area to the normal SST of the location beingimaged. Serial imaging also can assist the clinician in the ability torecognize improvement or regression of the AOI or wound over time.

The use of an LIR thermal and digital visual imager can be a usefuladjunct tool for clinicians with appropriate training to be able torecognize physiologic and anatomical changes in an AOI before itpresents clinically and also the status of the AOI/wound in a trendingformat. By combining the knowledge obtained from the images with acomprehensive assessment, skin and subcutaneous tissue evaluation, andan AOI or wound evaluation will assist the clinician in analyzing theetiology, improvement or deterioration, and the presence of infectionaffecting the AOI or wound.

The foundational scientific principles behind LIR thermographytechnology are energy, heat, temperature, and metabolism.

Energy is not a stand-alone concept. Energy can be passed from onesystem to another, and can change from one form to another, but cannever be lost. This is the First Law of Thermodynamics. Energy is anattribute of matter and electromagnetic radiation. It is observed and/ormeasured only indirectly through effects on matter that acquires, losesor possesses it and it comes in many forms such as mechanical, chemical,electrical, radiation (light), and thermal.

The present application focuses on thermal and chemical energy. Thermalenergy is the sum of all of the microscopic scale randomized kineticenergy within a body, which is mostly kinetic energy. Chemical energy isthe energy of electrons in the force field created by two or morenuclei; mostly potential energy.

Energy is transferred by the process of heat. Heat is a process in whichthermal energy enters or leaves a body as the result of a temperaturedifference. Heat is therefore the transfer of energy due to a differencein temperature; heat is a process and only exists when it is flowing.When there is a temperature difference between two objects or two areaswithin the same object, heat transfer occurs. Heat energy transfers fromthe warmer areas to the cooler areas until thermal equilibrium isreached. This is the Second Law of Thermodynamics. There are four modesof heat transfer: evaporation, radiation, conduction and convection.

Molecules are the workhorses and are both vehicles for storing andtransporting energy and the means of converting it from one form toanother. When the formation, breaking, or rearrangement of the chemicalbonds within the molecules is accompanied by the uptake or release ofenergy it is usually in the form of heat. Work is completely convertibleto heat and defined as a transfer due to a difference in temperature,however work is the transfer of energy by any process other than heat.In other words, performance of work involves a transformation of energy.

Temperature measures the average randomized motion of molecules (kineticenergy) in a body. Temperature is an intensive property by which thermalenergy manifests itself. It is measured by observing its effect on sometemperature dependent variable on matter (i.e. ice/steam points ofwater). Scales are needed to express temperature numerically and aremarked off in uniform increments (degrees).

As a body loses or gains heat, its temperature changes in directproportion to the amount of thermal energy transferred from a hightemperature object to a lower temperature object. Skin temperature risesand falls with the temperature of the surroundings. This is thetemperature that is referred to in reference to the skins ability tolose heat its surroundings.

The temperature of the deep tissues of the body (core temperatures)remains constant (within ±1° F./±0.6° C.) unless the person develops afebrile illness. No single temperature can be considered normal.Temperature measurements on people who had no illness have shown a rangeof normal temperatures. The average core temperature is generallyconsidered to be between 98.0° F. and 98.6° F. measured orally or 99.0°F. and 99.6° F. measured rectally. The body can temporarily tolerate atemperature as high as 101° F. to 104° F. (38.6° C. to 40° C.) and aslow as 96° F. (35.5° C.) or lower.

Metabolism simply means all of the chemical reactions in all of thecells of the body. Metabolism creates thermal energy. The metabolic rateis expressed in terms to the rate of heat release during the chemicalreactions. Essentially all the energy expended by the body is eventuallyconverted into heat.

Since heat flows from hot to cold temperature and the body needs tomaintain a core temperature of 37.0° C.±0.75° C., the heat is conservedor dissipated to the surroundings. The core heat is moved to the skinsurface by blood flow. Decreased flow to the skin surface helps conserveheat, while increased flow promotes dissipation. Conduction of the coreheat to the skin surface is fast, but inadequate alone to maintain thecore temperature. Heat dissipation from the skin surface (3 mmmicroclimate) also occurs due to the conduction, convection andevaporation.

Heat production is the principal by-product of metabolism. The rate ofheat production is called the metabolic rate of the body. The importantfactors that affect the metabolic rate are:

1. Basal Rate of Metabolism (ROM) of all cells of the body.

2. Extra ROM caused by muscle activity including shivering.

3. Extra ROM caused by the effect of thyroxine and other hormones to aless extent (i.e.: growth hormone, testosterone).

4. Extra ROM caused by the effect of epinephrine, norepinephrine, andsympathetic stimulation on the cells.

5. Extra ROM caused by increased chemical activity in the cellsthemselves, especially when the cell temperature increases.

Most of the heat produced in the body is generated in the deep organs(liver, brain, heart and the skeletal muscles during exercise). The heatis then transferred to the skin where the heat is lost to the air andother structures. The rate that heat is lost is determined by how fastheat can be conducted from where it is produced in the body core to theskin.

The skin, subcutaneous tissues and especially adipose tissue are theheat insulators for the body. The adipose tissue is important since itconducts heat only 33% as effective as other tissue and specifically 52%as effective as muscle. Conduction rate of heat in human tissue is 18kcal/cm/m2k. The subcutaneous tissue insulator system allows the coretemperature to be maintained yet allowing the temperature of the skin toapproach the temperature of the surroundings.

Blood flows to the skin from the body core in the following manner.Blood vessels penetrate the adipose tissue and enter a vascular networkimmediately below the skin. This is where the venous plexus comes intoplay. The venous plexus is especially important because it is suppliedby inflow from the skin capillaries and in certain exposed areas of thebody (hands-feet-ears) by the highly muscular arterio-venousanastomosis. Blood flow can vary in the venous plexus from barely abovezero to 30% of the total cardiac output. There is an approximateeightfold increase in heat conductance between the fully vasoconstrictedstate and the fully vasodilated state. The skin is an effectivecontrolled heat radiator system and the controlled flow of blood to theskin is the body's most effective mechanism of heat transfer from thecore to the surface.

Heat exchange is based on the scientific principle that heat flows fromwarmer to cooler temperatures. Temperature is thought of as heatintensity of an object. The methods of heat exchange are: radiation(60%), loss of heat in the form of LIR waves (thermal energy),conduction to a solid object (3%), transfer of heat between objects indirect contact and loss of heat by conduction to air (15%) caused by thetransfer of heat, caused by the kinetic energy of molecular motion. Muchof this motion can be transferred to the air if it is cooler than thesurface. This process is self-limited unless the air moves away from thebody. If that happens, there is a loss of heat by convection. Convectionis caused by air currents. A small amount of convection always occursdue to warmer air rising. The process of convection is enhanced by anyprocess that moves air more rapidly across the body surface (forcedconvection). This includes fans, air flow beds and air warming blankets.

Convection can also be caused by a loss of heat by evaporation which isa necessary mechanism at very high air temperatures. Heat (thermalenergy) can be lost by radiation and conduction to the surroundings aslong as the skin is hotter than the surroundings. When the surroundingtemperature is higher than the skin temperature, the body gains heat byboth radiation and conduction. Under these hot surrounding conditionsthe only way the body can release heat is by evaporation. Evaporationoccurs when the water molecule absorbs enough heat to change to gas. Dueto the fact water molecules absorb a large amount of heat in order tochange into a gas, large amounts of body heat can be removed from thebody.

Insensible heat loss dissipates the body's heat and is not subject tobody temperature control (water loss through the lungs, mouth and skin).This accounts for 10% heat loss produced by the body's basal heatproduction. Sensible heat loss by evaporation occurs when the bodytemperature rises and sweating occurs. Sweating increases the amount ofwater to the skins surface for vaporization. Sensible heat loss canexceed insensible heat loss by 30 times. The sweating is caused byelectrical or excess heat stimulation of the anterior hypothalamus preoptic area.

The role of the hypothalamus (anterior pre-optic area) in the regulationof the body's temperatures occurs due to nervous feedback mechanismsthat determine when the body temperature is either too hot or too cold.

The role of temperature receptors in the skin and deep body tissuesrelate to cold and warm sensors in the skin. Cold sensors outnumber warmsensors 10 to 1. The deep tissue receptors occur mainly in the spinalcord, abdominal viscera and both in and around the great veins. The deepreceptors mainly detect cold rather than warmth. These receptorsfunction to prevent low body temperature. These receptors contribute tobody thermoregulation through the bilateral posterior hypothalamus area.This is where the signals from the pre-optic area and the skin and deeptissue sensors are combined to control the heat producing and heatconserving reactions of the body.

Temperature Decreasing Mechanisms:

1. Vasodilation of all blood vessels, but with intense dilation of skinblood vessels that can increase the rate of heat transfer to the skineight fold.

2. Sweating can remove 10 times the basal rate of body heat with anadditional 1° C. increase in body temperature.

3. Decrease in heat production by inhibiting shivering and chemicalthermogenesis.

Temperature Increasing Mechanisms:

1. Skin vasoconstriction throughout the body.

2. Increase in heat production by increasing metabolic activity.

-   -   a. Shivering        -   i. 4 to 5 times increase    -   b. Chemical Thermogenesis (brown fat)        -   i. Adults 10-15% increase        -   ii. Infants 100% increase

LIR thermography evaluates the infra-red thermal intensity. Themicrobolometer is a 320×240 pixel array sensor that can acquire thelong-wave infrared wavelength (7-14 micron) (NOT near-infraredthermography) and convert the thermal intensity into electricalresistance. The resistance is measured and processed into digital valuesbetween 1-254. A digital value represents the long-wave infrared thermalintensity for each of the 76,800 pixels. A grayscale tone is thenassigned to the 1-254 thermal intensity digital values. This allows agrayscale image to be developed.

Using LIR thermography is a beneficial device to monitor metabolism andblood flow in a non-invasive test that can be performed bedside withminimal patient and ambient surrounding preparation. The ability toaccurately measure the LIR thermal intensity of the human body is madepossible because of the skins emissivity (0.98± is 0.01), which isindependent of pigmentation, absorptivity (0.98±0.01) reflectivity(0.02) and transmitability (0.000). The human skin mimics the“BlackBody” radiation concept. A perfect blackbody only exists in theoryand is an object that absorbs and reemits all of its energy. Human skinis nearly a perfect blackbody as it has an emissivity of 0.98,regardless of actual skin color. These same properties allow temperaturedegrees to be assigned to the pixel digital value. This is accomplishedby calibration utilizing a “BlackBody” simulator and an algorithm toaccount for the above factors plus ambient temperatures. A multi-colorpalate can be developed by clustering pixel values. There are noindustry standards how this should be done so many color presentationsare being used by various manufacturers. The use of gray tone values isstandardized, consistent and reproducible. Black is considered cold andwhite is considered hot by the industry.

An LIR camera has the ability to detect and display the LIR wavelengthin the electromagnetic spectrum. The basis for infrared imagingtechnology is that any object whose temperature is above 0° K radiatesinfrared energy. Even very cold objects radiate some infrared energy.Even though the object might be absorbing thermal energy to warm itself,it will still emit some infrared energy that is detectable by sensors.The amount of radiated energy is a function of the object's temperatureand its relative efficiency of thermal radiation, known as emissivity.

Emissivity is a measure of a surface's efficiency in transferringinfrared energy. It is the ratio of thermal energy emitted by a surfaceto the energy emitted by a perfect blackbody at the same temperature.

LIR thermography is a beneficial device to monitor metabolism and bloodflow in a non invasive test that can be performed bedside with minimalpatient and ambient surrounding preparation. It uses the scientificprinciples of energy, heat, temperature and metabolism. Throughmeasurement and interpretation of thermal energy, it produces imagesthat will assist clinicians to make a significant impact on wound care(prevention, early intervention and treatment) through detection.

In the method of grayscale digital thermographic imaging ofabnormalities of the skin and its subcutaneous tissue, the improvementcomprising: means for increasing and decreasing pixel value brightnessby adding a positive or negative offset to the raw pixel value.

One embodiment of the present invention is in the method of grayscaledigital thermographic imaging of abnormalities of the skin and itssubcutaneous tissues, the improvement comprising methods and apparatusfor defining pixel intensity variations of a long wave infrared image bymeasuring the thermal intensity ratio of the average of all pixel valuesfrom a skin abnormality region to the average of all pixel values fromunaffected skin regions.

Another embodiment of the present invention is in the method ofgrayscale digital thermographic imaging of abnormalities of the skin andits subcutaneous tissues, the improvement comprising methods andapparatus for maintaining the separation of a thermographic imager fromskin at a set distance by converging two light beams emanating from theimager at a point that is the set distance for the imager to be fromskin.

Another embodiment of the present invention is in the method ofgrayscale digital thermographic imaging of abnormalities of the skin atits subcutaneous tissues, the improvement comprising methods andapparatus for obtaining the linear length and width measurements ofabnormalities and their square area.

Another embodiment of the present invention is in the method ofgrayscale digital thermographic imaging of the skin and its subcutaneoustissues, the improvement comprising methods and apparatus forhighlighting the digital thermographic image of an area of skin to bemeasured and calculating the area of the highlighted portion of theimage in square centimeters by determining the total number of pixelshighlighted.

Another embodiment of the present invention is in the method ofgrayscale digital thermographic imaging of the skin and its subcutaneoustissues, the improvement comprising methods and apparatus for encirclingan area of interest and generating a histogram of the encircled area toproject the distribution of pixel values therein.

Another embodiment of the present invention is in the method ofgrayscale digital thermographic imaging of the skin and its subcutaneoustissues, the improvement comprising methods and apparatus for plottingprofile lines in or through an area of skin that is of interest andcomparing it with a corresponding profile line of normal skin.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood more fully from the detaileddescription given hereinafter and from the accompanying drawings of thepreferred embodiment of the present invention, which, however, shouldnot be taken to limit the invention, but are for explanation andunderstanding only.

In the drawings:

FIG. 1 shows a medical long wave infrared (LIR) and visual viewscompared.

FIG. 2 shows a thermal span with default configuration settings.

FIG. 3 shows an effect of adding a positive offset of the thermal span.

FIG. 4 shows effect of adding negative offset on the thermal span.

FIG. 5 shows a thermal image of a hand taken with default settings.

FIG. 6 shows a thermal image of the hand when a positive offset isadded.

FIG. 7 shows a normal and abnormal selections made from a thermal imageand the corresponding results.

FIG. 8 shows an original image (left side) and thermal image (rightside-zoomed in) with abnormal selections made.

FIG. 9 shows a schematic representing pixel intensity recognition(zoomed).

FIG. 10 shows a diagram of laser lights implementation.

FIG. 11 shows an experimental setup used to determine digital camera andlong-wave infrared microbolometer angels of inclination.

FIG. 12 shows an embodiment of laser lights at an 18 inch distance.

FIG. 13 shows a length and width measurements form an area of interest.

FIG. 14 shows a schematic representing pixel intensity recognition(zoomed.)

FIG. 15 shows a periwound region including the wound base highlighted asarea of interest and the results obtained for the area selected.

FIG. 16 shows an area including normal, periwound and the wound baseregions highlighted as area of interest and the corresponding resultsobtained for the area selected.

FIG. 17 shows wound histograms.

FIG. 18 shows normal histograms.

FIG. 19 shows a profile line showing the variation in the grayscalevalues along the line drawn over an area of interest.

FIG. 20 shows comparing the Profile Line with the Reference Line.

FIG. 21 shows a thermal Profile Line.

FIG. 22 shows a figure illustrating the formula for calculating areaunder the curve.

FIG. 23 shows calculating areas above and below the selected normal.

FIG. 24 shows a profile Line drawn through three fingers.

FIG. 25 shows a profile Line plot on a graph.

Corresponding reference characters indicate corresponding partsthroughout the several views. The exemplary embodiments set forth hereinare not to be construed as limiting the scope of the invention in anymanner.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention will be discussed hereinafter in detail in termsof various exemplary embodiments according to the present invention withreference to the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present invention. It will be obvious,however, to those skilled in the art that the present invention may bepracticed without these specific details. In other instances, well-knownstructures are not shown in detail in order to avoid unnecessaryobscuring of the present invention.

Thus, all of the implementations described below are exemplaryimplementations provided to enable persons skilled in the art to make oruse the embodiments of the disclosure and are not intended to limit thescope of the disclosure, which is defined by the claims. As used herein,the word “exemplary” or “illustrative” means “serving as an example,instance, or illustration.” Any implementation described herein as“exemplary” or “illustrative” is not necessarily to be construed aspreferred or advantageous over other implementations. Moreover, in thepresent description, the terms “upper”, “lower”, “left”, “rear”,“right”, “front”, “vertical”, “horizontal”, and derivatives thereofshall relate to the invention as oriented in FIG. 1.

Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply exemplary embodiments of the inventive concepts defined in theappended claims. Hence, specific dimensions and other physicalcharacteristics relating to the embodiments disclosed herein are not tobe considered as limiting; unless the claims expressly state otherwise.

Thermal images taken of the skin surface are constructed by passivelyreading emitted radiant energy formed by the subcutaneous tissue and theskin tissue by detecting wavelengths in the long-wave infrared range(LIR) of 7-14 microns, and then in real time converting these valuesinto pixels within a digital image. The value assigned to the pixelindicates the thermal intensities of a particular area of the skin whenimaged. The thermal images in this embodiment are presented in digitalunsigned (not having a plus or minus sign) 8-bit grayscale with pixelvalues ranging from 0-254, however these same techniques work withimages of varying color resolutions. These images could be stored in thedata bank along with the information about the data the image hascaptured so that it can be retrieved by a clinician for future reviewand analysis. Generally, the unaffected skin thermal intensity will be auniform gray color within a range of +/−3 to 6 pixel values, which isequal to 0.25 to 0.5 degrees centigrade. Abnormally hot areas of theskin will be represented by patches of increasingly white pixels, whileabnormally cold areas will be represented by increasingly dark patchesof pixels.

The use of LIR (7-14 microns) imaging along with visual digital imagingallows both physiologic (long-wave infrared and visual) and anatomicassessment of skin and subcutaneous tissue abnormalities and or existingopen wounds. The gradiency of the thermal intensity, not the absoluteamount of intensity, is the important component of the long-wave thermalimage analysis that will allow the clinician to evaluatepathophysiologic events. This capability is beneficial to the clinicianin the prevention, early intervention and treatment assessments, of adeveloping existing condition caused by, but not exclusively, wounds,infection, trauma, ischemic events and autoimmune activity.

Utilizing temperature values (F.°, C.°, and Kelvin) as the numericalvalues of LIR thermal heat intensity is complicated due to the need tohave a controlled environment. This is required since the value of thetemperature scales is affected by ambient temperature, convection ofair, and humidity. These variables would need to be measured anddocumented continuously if temperature values were used. Also theemissivity, absorptivity, reflexivity and transmitability of the skinand subcutaneous tissue can be affected by skin moisture, scabbing,slough and/or eschar formation in an open wound.

To address this problem the imager utilizes the raw data captured by themicrobolometer. This data is utilized in determining pixel valuesrelating to the intensity of the thermal energy from the long-waveinfrared electromagnetic radiation spectrum being emitted by the humanbody. The pixel gradient intensities are represented for visualizationby the grayscale presentation.

The pixel values in the grayscale thermal images also vary with thevarying conditions mentioned above and hence the algorithms proposed inthis application use the average pixel value of the unaffected skinregion for that patient on the day the image was taken as a referencepoint for all the calculations.

Combining the above technique with suggested usage of unaffected skinand subcutaneous tissue in the proximity of an abnormality of askin/subcutaneous tissue location as a real time control helps tominimize the variability and time consuming requirements in utilizingtemperature scales.

There is a difference in the LIR thermal intensity regions of the humanbody. LIR images have a defined pixel intensity range that is based onthe specific usage of an LIR image. In the arena of skin andsubcutaneous tissue LIR thermal gradiency, the range is withinhomeostasis requirements to sustain life. The visualization of pixelintensities is accomplished by the use of a standardized 8-bitgrayscale. Black defines cold, gray tones define cool and/or warm andwhite defines hot. When the imager is used for capturing extremely hotor extremely cold regions that fall outside the thermal range of theimager the pixel values reach the saturation point and it becomesextremely difficult for the human eye to differentiate variations in thepixel values.

This situation can be addressed by utilizing a visualization techniquethat increases the pixel values to create a positive offset to make theimage look brighter. In the same manner a negative offset can be used todecrease the pixel values to make the image look darker.

A. Increasing and Decreasing Pixel Value Brightness by Adding a Positiveor Negative Offset to the Raw Pixel Value.

The positive and negative offset can be utilized to assist invisualizing the area of the body being imaged. The usage of the offsetscan then be documented as being used at the time the image is initiallytaken. The default gray tone that represents the actual pixel values isthe raw data being stored in the data bank so future analysis can beperformed by clinicians at a later time and/or in another location. Thedefault grayscale data is accompanied by documentation of the use ofeither the positive or negative offset process. This allows for enhancedvisualization of black and white extremes in the grayscale image. Thegoal is to visually enhance the image at either the lower or higher sideof the thermal intensity range without altering the original image.

Referring to FIG. 2, the thermal imager could be configured to capturethe thermal intensity variation information within a certain range ofthermal intensity. Configuration settings were carefully chosen suchthat they capture all thermal intensity variations between 19° C. (66.2°F.) to 40.5° C. (104.9° F.), which covers most of the human body'sphysiologic thermal intensity range. When the thermal intensity of anarea of interest gets close to 19° C. (66.2° F.), the pixel values inthe grayscale thermal image appear darker and reach a low saturationpoint. When the thermal intensity drops below 19° C. (66.2° F.), thethermal image would still appear dark but would not get any darker asthe low saturation point has already been reached. Similarly as thethermal intensity of an area of interest starts increasing, the thermalimage starts looking brighter. As the thermal intensity gets close to40.5° C. (104.9° F.), the thermal image reaches the high saturationpoint and the pixel values in the grayscale image reach the maximumvalue. As the thermal intensity goes beyond 40.5° C. (104.9° F.), eventhough the thermal intensity of the area of interest is increasing, thethermal image would not appear any brighter as the high saturation pointhas been reached.

Even though the thermographic imager can pick up thermal intensities aslow as 19° C. (66.2° F.) the grayscale thermal image for an area ofinterest at that thermal intensity would appear too dark. The human eyeis not able to visualize the variation of the 254 pixel values includedin the standardized grayscale. This might cause problems whenthermographic images are taken on areas of the human body with decreasedmicrocirculation, (i.e., the fingers, toes, etc.) or areas withcartilage (i.e., the tip of the nose, ear, etc.). These body locationsare usually the coldest on the skin surface thermal intensity and wouldappear darker in the thermal images.

To solve this problem, a novel technique has been developed to increaseor decrease the brightness of the pixel values by adding a positive ornegative offset to the raw pixel values. The positive or negative offsetallows an enhanced visualization of the black or white extremes in agrayscale image. The goal here is to visually enhance the image ateither the lower or higher end of the thermal intensity range withoutaltering the original image.

With default configuration settings and at a room thermal intensity of22.11° C. (71.8° F.), the thermal intensity range picked up by thethermal imager was as illustrated in FIG. 2.

A low saturation grayscale value of 1 was reached at 19° C. (66.2° F.)and the high saturation grayscale value of 254 was reached at 40.5° C.(104.9° F.), giving a thermal span of 21.5 degrees. The maximumresolution is then 0.0846° C. with in the image.

Formula

Thermal Span (Thermal intensity range picked up by an imager)=(Thermalintensity at which the pixels reach the high saturation value)−(Thermalintensity at which the pixels reach the low saturation value):

${{Maximum}\mspace{14mu} {resolution}} = \frac{( {{{High}\mspace{14mu} {saturation}\mspace{14mu} {temperature}} - {{low}\mspace{14mu} {saturation}\mspace{14mu} {temperature}}} )}{{Resolution}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {gray}\mspace{14mu} {scale}\mspace{14mu} {image}}$

For an 8-bit grayscale image the resolution is fixed at 254 parts.

Adding a Positive Offset (Example of Use)

When a positive offset +20 was added to all the pixels to make the imagelook brighter the imager reached the low saturation grayscale value of21 at 19° C. (66.2° F.). Since a value of +20 is added to all thepixels, the grayscale value can only go as low as 21 instead of 1 asobtained with default settings. This lowest grayscale value was obtainedat the same thermal intensity (19° C.) as the low saturation thermalintensity obtained with default settings. This indicates that adding anoffset will only increase the pixel value making it look brighter sothat small variations in the pixel values could be visually seen. Thisdoes not enable the thermal imager to pick up thermal intensities lowerthan what can be read with default settings.

With positive offset added, the image appears brighter and reaches thehigh saturation value at a thermal intensity lower than the highsaturation thermal intensity obtained with default settings. The imagerreached the high saturation thermal intensity at 39° C. (102.2° F.)instead of 40.5° C. (104.9° F.), as obtained with default settings.

FIG. 3 shows the thermal intensity range that is detected when apositive offset is added to the default pixel value configurationsetting.

The thermal span is reduced to 20 degrees instead of 21.5 degrees asobtained with default settings when a positive offset was added. Themaximum resolution increased to 0.0855° C. which gives more definitionto the pixels within the image.

Adding a Negative Offset (Example of Use)

Adding a negative offset to the raw signal coming from the imager makesthe thermal image look darker, improving the visualization of the hot(bright) areas. When an offset of −20 was added to the original signalthe pixel values reached the low saturation value of 1 at 20.5° C.(68.9° F.) instead of 19° C. (66.2° F.). Since the thermal images arcsaved as unsigned 8-bit grayscale images with pixel values ranging from1-254, if the values fall outside this range they would be mapped to 1or 254. So when a negative value is added, pixels with values less than20 would become negative and were mapped back to 0 so that the pixelvalues always stay in the range of 1-254. Similarly on the high end thepixel values reached the highest saturation value of 234 at 40.5° C.(104.9° F.). With a negative offset added the highest the pixel valuescan go up to is 234 instead of 254. This high saturation occurred at thesame thermal intensity as obtained with default settings.

FIG. 4 shows the effect of adding a negative offset on the thermalintensity range that could be picked up by the thermal imager.

The thermal span is reduced to 19 degrees giving a maximum resolution of0.0855° C. within the image.

By choosing a suitable offset (positive or negative) value thevisualization of an image is enhanced by increasing the resolutionwithin the image. This concept has been implemented and proven by theresearched thermal imaging. An offset of 20 was chosen as an example.This could change based on the requirements. FIG. 5 below shows athermal image of a hand taken with default settings. FIG. 6 below showsan example of the effect on the thermal image when a positive offset isadded to the pixel values at default settings to improve thevisualization of the image.

B. Defining Pixel Intensity Variations in the Long-Wave Infrared Image

To assist the clinician to define the pixel intensity variations of thelong-wave infrared image to see how thermal intensity is varying acrossthe skin area of images taken, as well as previous thermal images of thesame location, an inventive technique has been developed that measuresthe thermal intensity ratio. This gives the clinician the ability tolook at the images captured with the thermal imager and choose pixelpoints in the image utilizing non-zoomed and zoomed presentations of theimage that represent skin and subcutaneous tissue surrounding the areaof interest. The clinician also has the ability to select the tissue inwhich an injury/wound exists as shown in FIGS. 7 and 8. The zoomedcapability allows the clinician to be very precise in the selection ofthe pixels used to measure thermal intensity. The zoomed feature isparticularly useful because of the complexity of various wound types.For example, the wound base and periwound can be disorganized (acute andchronic condition, etc.), organized (wound resurfacing or repairing,etc.), and/or infected (wound base infection with and without periwoundcellulitis, etc.).

FIG. 7 shows a non-zoomed thermal image with unaffected and abnormalselections. The ‘X’ marks represent the unaffected skin, the asterisksymbol represents the wound base and the circle marks represent theperiwound.

FIG. 8 shows an original and zoomed thermal image with abnormalselections. The table in the image shows selected points on the thermalimage with their corresponding grayscale values.

FIG. 9 shows a schematic representing pixel intensity recognition(zoomed).

Pixels with uniform gray color represent the unaffected skin andsubcutaneous tissue. If the pixel value is too high then it can be anindication of an infection developing in that area. The wound base isusually colder than the unaffected skin's thermal intensity and isrepresented with darker pixels on a thermal image. The pixel values fora periwound area are usually higher than the wound base pixel value andless than the pixel value associated with the unaffected tissue as theirthermal intensity falls between the unaffected skin thermal intensityand the wound base thermal intensity.

The display of pixel value associated with each pixel selection madecould help a clinician make a decision on whether an area of interest ispresent. This allows the following calculations to be performed:

Wound Base to Unaffected Ratio:

${{Wound}\mspace{14mu} {base}\mspace{14mu} {to}\mspace{14mu} {unaffected}\mspace{14mu} {ration}} = \frac{\begin{pmatrix}{{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}} \\{{values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {wound}\mspace{14mu} {base}\mspace{14mu} {region}}\end{pmatrix}}{( {{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {unaffected}\mspace{14mu} {region}} )}$

Wound base regions are usually colder than the unaffected skin thermalintensity, causing the pixel values for the wound base regions to belesser than the pixel values for the unaffected skin regions in an LIRimage.

If the wound base to unaffected ratio is less than 1, it is anindication that the wound base is colder than the unaffected regionaltissue. If the ratio is greater than 1, it is an indication that thewound base area is hotter than the regions selected as unaffected skinarea. In summary, the closer the value gets to 1, the closer the woundbase area is getting to unaffected skin.

Periwound to Unaffected Ratio:

${{Periwound}\mspace{14mu} {to}\mspace{14mu} {unaffected}\mspace{14mu} {ratio}} = \frac{( {{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {periwound}\mspace{14mu} {region}} )}{( {{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {unaffected}\mspace{14mu} {region}} )}$

If the periwound to unaffected ratio is less than 1, it indicates thatthe periwound is colder than the unaffected skin area. If the ratio isgreater than 1, it is an indication that the periwound area is hotterthan the regions selected as unaffected skin area. In summary, thecloser the value gets to 1, the closer the periwound area is getting tounaffected skin.

Periwound to Wound Base Ratio:

${{Periwound}\mspace{14mu} {to}\mspace{14mu} {wound}\mspace{14mu} {base}\mspace{14mu} {ratio}} = \frac{( {{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {periwound}\mspace{14mu} {region}} )}{( {{Average}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {wound}\mspace{14mu} {base}\mspace{14mu} {region}} )}$

The ratio greater than 1 indicates that the periwound region is hotterthan the wound base region and the ratio less than 1 indicates that thewound base region is hotter than the periwound region. In summary, thecloser the ratio gets to 1, the closer the wound base and peri woundvalues get to each other.

By monitoring these ratios the clinician could get a better idea on thestatus of the wound.

C. Maintaining Separation of the Imager from Target

Long-wave infrared and visual images must be consistently taken at apredetermined distance, typically 18 inches. This capability allowsmeasurements to be obtained by length×width, by linear measurement, andby encirclement of the area of interest and or wound. This informationis considered to be the gold standard of the wound care industry indetermining the progression or regression of abnormalities.

Thermal and visual cameras are used for capturing images of areas ofinterest, such as wounds in a real time fashion (i.e., bedside oroutpatient clinic). Cameras are built so that they can communicate withcomputer via a USB connection and capture both visual and thermal imagesby clicking the trigger button on the camera.

All the images need to be captured at a certain distance from the bodypart and a standard distance of 18 inches between the camera and thebody part was found in testing done to date to be an ideal distance.Several methods were used in order to measure this distance.

As a first attempt an antenna of length 18 inches was placed on thecamera core that could be extended out. When the end of the antennatouched the body part the standard distance was known to have beenattained, indicating that the camera is ready for capturing images. Theadverse effects of using an antenna for measuring the distance were thatthe antenna would be touching the body part giving rise to possible riskof contamination, and also that the antenna comes into the field of viewwhen the image is being captured causing problems with visualization.

To overcome these problems the antenna method was replaced with a moresophisticated method using ultrasonic sound waves. An ultrasonictransducer placed on the camera core would release ultrasonic soundwaves for transmission in the desired path and when these waves hit thetarget, which would be the body part in our case, and ultra sonic soundwaves would be reflected back from the target in the transmission path.The received ultrasonic sound waves can then be converted into anelectrical signal that can be processed by a processor to providedistance information. The distance can be computed by using the timeperiod from the middle time value of the received electrical signal tothe middle time value of the transmitted signal. Whenever this distanceequals the standard distance of 18 inches a reduced audible noise willbe generated, indicating that the camera is ready to capture an image.

Even though the ultrasonic sound wave method has been proven to besuccessful and has been used in various applications to date formeasuring the distances, it was never used in the medical field atbedside as a tool for capturing visual and thermal images.

Limitations of using the ultrasonic method included the complexity ofwiring and the size of the apparatus used for measuring the distance andthen displaying it so that the end user can see how far the camera isfrom the target. The other major limitation arose with the presence ofan object in between the camera and the target. When there is an objectin the path, part or all of the waves will be reflected back to thetransmitter as an echo and can be detected through the receiver path. Itis difficult to make sure that the received ultrasonic sound waves wereactually reflected by the target and not by any other object in thepath.

The ultrasonic measuring of the distance was replaced with the use oftwo Class I Laser LED lights. Two Class I A, or of less strength, lasersand/or LED modified lights are used in this method. These lasers emitnarrow light beams as opposed to diffused light. They are placed oneither side of the camera lens. When the distance between the camera andthe target is less than 18 inches the lights coming from these lasersfall on the target as two spots separated by a distance and thisdistance will keep decreasing as the camera is moved toward from thetarget. When the distance between the camera and the target equal 18inches the lights from these two light sources will coincide, indicatingthat the focus point has been achieved and that the camera is ready forcapturing images. The distance between the two light beams startsincreasing again when the distance between the camera and the targetincreases to the standard 18 inches.

FIG. 10 explains the above embodiment in more detail, where IFRrepresents the long wave infrared microbolometer and D represents thevisual digital camera, and L represents the laser lights.

Depending on how far the laser lights arc going to be from themicrobolometer and the distance between the microbolometer and thetarget the angles at which the lasers need to be inclined will change.

The digital camera ‘D’ is also going to be placed at around 1.5 inchesaway from the long-wave infrared microbolometer and in order to makeboth the digital and the long-wave infrared microbolometer to have thesame focus point and field of view the digital camera needs to beinclined at an angle.

The experimental setup of FIG. 11 that was used in order to determinethe angle of inclination is as shown.

FIG. 12 is a representation of an embodiment that uses 18 inches as thedesired distance in a clinical setting. By changing the angles of theClass 1 Lasers this distance can be increased or decreased to meet otherneeds or requirements determined by the clinician.

D. A Consistent Technique to Obtain Wound Measurement Length and WidthLinearly Using a Thermal Image

To assist clinicians with maintaining accuracy and consistency whenmeasuring a wound, a novel technique has also been developed to obtainconsistent linear wound measurements (length and width) using a thermalimage. It allows a clinician to follow a standard of care to determinethe progression and regression of the wound by measuring length andwidth and area.

To be able to obtain the measurements of a wound from an image thenumber of pixels available per centimeter or per inch in that imageneeds to be known. When images are always taken from a standard distancethe number of pixels per inch in that image always remain constant, andthey change with the change in the separation distance between theimager and the target.

The imager has been designed such that the separation distance betweenthe imager and the target is always maintained at 18 inches. Severaltechniques like using a measuring tape, using ultrasound and using Class1 lasers have been tried and tested to date to maintain this standarddistance. The final version of the imager makes use of two Class lasersmounted inside the imager at an angle such that the laser beams emittedfrom these two lasers always converge at 18 inches from the front of thecamera.

For an image taken at a distance between the object being imaged and theimager that is exactly 18 inches there would be in the imageapproximately 40 pixels per inch. This distance can be changed, but ateach distance the number of pixels needed to equal 1 cm or 1 inch mustbe measured and tested. The selected distance must be noted to maintainreproducibility. For the calculation of length and width of the wound,when a line is drawn across the area of interest by measuring the numberof pixels covered across this line and using a conversion formula themeasurement in pixels could be converted into inches or centimeters. Foran image taken at 18 inches from the target, a line that is 40 pixels inlength would be approximately 1 inch on the measuring scale and usingthe inch to centimeter conversion the length could then be convertedinto centimeters.

Algorithm for Measuring Length and Width of an Area of Interest (inCentimeters)

Draw a line across the image that represents the length or width of thearea of interest that needs to be measured.

Note the x and y coordinates of the starting and ending points of thisline.

If (x1,y1) represent the x and y coordinates of the starting point ofthe line and (x2,y2) represent the x and y coordinates of the end pointof the line then the distance between these two points (length of theline in pixels) can be measured as:

${{Length}\mspace{14mu} ( {{or}\mspace{14mu} {width}} )\mspace{14mu} {in}\mspace{14mu} {pixels}} = \sqrt{( {{x\; 2} - {x\; 1}} )^{2} + ( {{y\; 2} - {y\; 1}} )^{2}}$${{Length}\mspace{14mu} ( \quad {or}\mspace{14mu} {width}\mspace{14mu} {in}\mspace{14mu} {inches}} = {{\frac{{Length}\mspace{14mu} {in}\mspace{14mu} {pixels}}{40}{Length}\mspace{14mu} ( {{or}\mspace{14mu} {width}} )\mspace{14mu} {in}\mspace{14mu} {centimeters}} = \frac{{Length}\mspace{14mu} {in}\mspace{14mu} {pixels}}{15.7480}}$

As per Minimum Data Set (MDS) Version 3.0, it is recommended that thelength of a wound is always measured as the longest length drawn fromhead to toe and width is measured as the widest width drawn side to sideperpendicular to the length. The x or y coordinates of the end point ofthe line representing the length or the width line could be adjusted tomake sure the lines are exactly vertical or horizontal which would intum make them perpendicular to each other.

Using the length and the width measurements (length×width) area could becalculated.

By monitoring the thermal images taken on day to day basis, and bymeasuring the length and the width for the area of interest each day,the status of the wound could be monitored to see whether there has beena progression or regression in the status. FIG. 13 shows the length andwidth measurement in centimeters obtained for an image with an area ofinterest on a heel.

E. Highlighting the Wound Base, Periwound and Unaffected Regions toMeasure and Calculate the Square Areas Thereof by Using the Number ofPixels Highlighted

A novel technique has been developed that gives the clinician theability to highlight a wound base, periwound or unaffected regions andto measure the area in square centimeters. This will assist theclinician in looking at the overall status of the wound, and evaluatingits progression or regression.

The total number of pixels enclosed within the highlighted area could beused for calculating the area of the region selected.

A test target of size 1.5 inch×1.5 inch was used. With the imager at 18inches from the test target, images were captured.

The area of test target=1.5 inch×1.5 inch=2.25 square inches or 3.81cm×3.81 cm=14.5161 square cm.

For an image taken at 18 inches from the target there would beapproximately 40 pixels per inch. So there would be approximately 60pixels in 1.5 inches.

The area of the test target obtained from the image=60 pixels×60pixels=3600 pixels. A total of 3600 pixels were enclosed inside the areaof the test target. So 3600 pixels

14.5161 square cm

For an unknown area of interest, if “Y” is the number of pixels enclosedinside that area then the surface area in square centimeters for thatregion would be equal to:

${{Area}\mspace{14mu} {in}\mspace{14mu} {square}\mspace{14mu} {centimeters}\mspace{14mu} {for}\mspace{14mu} {the}\mspace{14mu} {highlighted}\mspace{14mu} {region}} = \frac{( {Y \times 14.5161} )}{3600}$

For the region highlighted as the wound base, area in square cm's andaverage of all the pixel values falling inside the highlighted regionare calculated and displayed in the picture as shown in FIG. 14.

Periwound area represents the area surrounding the wound base. Byhighlighting the area that includes the wound base and the periwoundarea surrounding it as shown in FIG. 15, and by counting the number ofpixels enclosed in that region, the area of the highlighted region couldbe calculated in square centimeters. The periwound area could then beobtained by subtracting the wound base area from the area that includesboth the peri wound and the wound base areas.

By including the unaffected skin and subcutaneous tissue surrounding thewound in the highlighted area of interest, the unaffected area could becalculated in square centimeters. The unaffected area could then beobtained by subtracting the wound base and periwound area from theregion selected that includes unaffected, periwound and the wound baseareas.

FIG. 16 below shows the calculations displaying the highlightedunaffected area and the various calculations obtained from thehighlighted regions.

F. Obtaining the Average Pixel Value and the Plus/Minus Variance byEncircling the Area of Interest/Wound

By utilizing the novel techniques above, not only can the area becalculated, but simultaneously the average pixel value of each area canbe calculated. This will allow the clinician to evaluate the status ofthe area of interest or wound not only in micro (focused technique,above) but also in the macro using the technique described below. Thecombination of these two assessments will give a better overallunderstanding of the areas of interest where the abnormality or woundhas been identified. From this average data the ratio concept discussedabove can also be used to evaluate the macro (overall) look at an areaof interest or wound, specifically if the wound is becoming organized,i.e., is it improving, becoming infected, or regressing (getting worse).See Table 1 below.

TABLE 1 Summarizing the results obtained from the highlighted nomzal,periwound, and wound base regions Normal Periwound Wound Base Area insq. cm 29.03 16.66 7.71 Average pixel value 125.17 103.82 61.09 Minimumand [Various range] [Various range] [Various range] maximum pixel values

Some of the other measurements that could be done to keep track of thestatus of an area of interest include calculating the average, minimumand maximum of all the pixel values falling inside the highlighted area.

${{Average}\mspace{14mu} {pixel}\mspace{14mu} {value}} = \frac{\begin{matrix}{{Sum}\mspace{14mu} {of}\mspace{14mu} {pixel}\mspace{14mu} {values}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixels}} \\{{that}\mspace{14mu} {fall}\mspace{14mu} {inside}\mspace{14mu} {the}\mspace{14mu} {highlighted}\mspace{14mu} {area}\mspace{14mu} {of}\mspace{14mu} {interest}}\end{matrix}}{\begin{matrix}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}} \\{{falling}\mspace{14mu} {inside}\mspace{14mu} {the}\mspace{14mu} {highlighted}\mspace{14mu} {area}}\end{matrix}}$

For a highlighted area of interest a histogram can be generated toprovide graphical representation of distribution of pixel values withinthat area.

Algorithm for Generating Histograms.

Highlight the area of interest for which a histogram needs to begenerated

Determine the total number of bins/buckets into which the data needs tobe divided into. There is no best number of bins, and different binsizes can reveal different features of the data.

Bin size can be calculated as

${{Bin}\mspace{14mu} {size}} = \frac{{Maximum}\mspace{14mu} {value}\text{\textasciitilde}{Minimum}\mspace{14mu} {value}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {bins}}$

For a thermal image the pixel values always range between 0 and 255.

Create an empty array of size equal to the total number of bins.

Check to see if a pixel falls inside the highlighted area of interestand if it does note the pixel value.

The bin number into which this pixel value falls under can be calculatedusing the formula.

${{Bin}\mspace{14mu} {number}} = \frac{{{Pixel}\mspace{14mu} {value}} - {{Minimum}\mspace{14mu} {value}}}{{Bin}\mspace{14mu} {Size}}$

Increment the value of the array at the index [Bin number −1], sincearrays are zero based, by one.

Repeat the steps 5-7 for all the pixels in an image.

After checking all the pixels in an image, plot the array to generate ahistogram.

Clinical Significance of Histograms.

Distribution of pixel values as projected by the histograms for ahighlighted area of interest provides more in depth information aboutthe signature of a wound. If the histogram plot is more spread out itindicates there is a large variation in the pixel values and hencetemperatures within the highlighted area as shown in FIG. 17. As theplot starts getting more and more narrow it is an indication that allthe pixels inside the highlighted portion are getting close to eachother and the temperature inside the highlighted portion is starting toget saturated towards a single temperature value. If the saturationoccurs at a higher pixel value then it is an indication that all thepixels inside the highlighted portion are getting very hot compared tothe selected normal reference point. Similarly if the saturation occursat a very low pixel value then all the pixels inside the highlightedarea are getting very cold. FIG. 17 shows some sample histogramsgenerated for an image with a highlighted area of interest.

G. Creating Profile Lines in and Through an Area of Interest/Wound andComparing with Profile Lines Trough Reference Areas

A novel feature has been developed to assist a trained clinician tobetter track a wound by utilizing the ability to plot profile linesthrough the wound. These plots show the variation in the pixel valuesacross the wound. Since the thermal intensity is directly related to thegrayscale pixel values in an image, these plots can be used to monitorhow the thermal intensity is varying across an area of interest orwound. This allows the clinician to dissect the wound in precise fashionso the pathophysiologic status of the wound can be assessed andquantified.

Profile lines can be plotted by simply drawing a line across the area ofinterest. FIG. 18 below shows an example of the profile line generatedby drawing a line across the wound present on the heel. As seen in theplot there is a huge drop in the pixel value/thermal intensity acrossthe wound base region and the value starts increasing as the line ismoving away from the wound base and entering areas with unaffected skintissue.

As the wound starts healing the difference between the pixel value forthe unaffected tissue and the pixel value from the wound base startsdecreasing and hence the drop seen in the graph starts decreasingindicating that the wound is healing and is starting to get close to theunaffected skin tissue.

If the drop in the pixel values starts increasing, when plots aregenerated for images taken on timely basis then it is an indication thatthe wound is deteriorating and that the clinician needs to tum tostrategies to facilitate wound healing.

Algorithm for Generating the Profile Lines

Draw a line across the area of interest for which the profile lines needto be plotted.

Record the X and Y locations of the starting and end points of theprofile line. Let (x1, y1) represent the coordinates of the startingpoint and (x2, y2) represent the coordinates of the end point.

deltaX=absolute value of (x2−x1); deltaY=absolute value of (y2−y1)

length of the line=L=√{square root over ((x2−x1)²+(y2−y1)²)}

x_increment=deltaX/L

y_increment=deltaY/L

Round off L to the nearest integer and then increment by 1; L=L+1

Create a new array to hold the pixel values that fall across the profileline. Let us call this array.

‘Pixel Values’.

Pixel_values(1)=pixel value of the image at the location x1, y1. Add thex_increment and y_increment to the original x1 and y1 respectively anduse these as new values for x1 and y1. So x1=round (xi+x_increment)y1=round (y1+y_increment)

Create a new counter variable, let us call it ‘i’

i=1;

While ((i<L) && (x1, y1 fall within the size of the image)

Pixel_values (i+1)=pixel value of the image at the location x1, y1;

x1=round(x1+x_increment);

y1=round(y1+y_increment);

i=i+1;

End

The array ‘Pixel_values’ should contain values of all the pixels thatrepresent the profile line.

Plotting the values in the array ‘Pixel_values’ gives the plot for theprofile line drawn across the area of interest (as shown in the figureabove).

Images taken using a thermal imaging camera can be analyzed and trackedto monitor the status of wounds.

Profile lines provide a tool for monitoring variations in pixel valuesand hence the temperatures across the abnormal areas of interest. Thesevariations can be compared against the pixel value representingunaffected region for that patient by selecting a region on the imagethat represents unaffected skin.

Comparing the Profile Line with the Reference Line Representing theUnaffected Skin for that Patient.

Comparing the pixel values of the pixels falling across the profile linewith the reference pixel value that represents unaffected skin for thatpatient gives a measure of how close or far away the profile line pixelvalues are from the selected reference line.

For selecting unaffected regions a circle can be drawn on the image thatcomprises of only the unaffected pixels and does not include anyabnormalities or the background. Once a circle has been drawnrepresenting unaffected skin for the patient, average of all the pixelsfalling inside the circle can be calculated as follows:

${{Average}\mspace{14mu} {Normal}\mspace{14mu} {pixel}\mspace{14mu} {value}} = \frac{\begin{matrix}{{Sum}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}} \\{{inside}\mspace{14mu} {the}\mspace{14mu} {circle}\mspace{14mu} {representing}\mspace{14mu} {Normal}}\end{matrix}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {inside}\mspace{14mu} {the}\mspace{14mu} {circle}}$

To determine whether a pixel falls inside a circle of radius ‘r’calculate the distance between the center of the circle and thecoordinates of the pixel point using the formula

Distance=√{square root over ((x2−x1)²+(y2−y1)²)}

where (x1, y1) represent the X and Y coordinates of the center of thecircle and (x2,y2) represent the X and Y coordinates of the pixel.

If the distance is less than the radius of the circle then that pixelfalls inside the circle representing unaffected skin area.

Once the average normal pixel value has been calculated this value canbe plotted on the chart along with the profile line as shown in theFIGS. 20 and 21.

By comparing the profile line with the normal line the status of thearea of interest can be tracked. As the profile line gets closer to thereference line it indicates that the area of interest is improving andis getting closer to the normal skin characteristics.

The portions below the reference line represent the segments of theprofile line where the pixel values are lower (colder) than the selectednormal reference point. Similarly the points falling above the referenceline represent the portion of the profile that is hotter than theselected normal reference.

Once a normal reference point has been chosen and a profile line hasbeen drawn several parameters can be calculated to compare the profileline signature with the reference line signature. By tracking how thesevalues change on day to day basis the status of the wound could betracked.

Some of the factors that could be calculated to compare the profile linewith the reference line include area above and below the reference line,maximum rise and drop, average rise and drop from the reference lineetc.

The area calculations also give a measure of the portion of the profileline that falls above or below the normal reference line. The area thatfalls above the reference line indicates the regions that have a pixelvalue higher that the reference point and hence are at a highertemperature. The area below the reference line shows the portion of theprofile line that has temperatures lower than the selected reference.

The areas can be calculated using the Trapezoidal rule of calculatingarea under the curve.

Calculating Area Above and Below the Reference Line

The area between the graph of y=f(x) and the x-axis is given by thedefinite integral in FIG. 22 (Reference:http://www.mathwords.com/alarea_under a_curve.htm) This formula gives apositive result for a graph above the x-axis, and a negative result fora graph below the x-axis.

Note: If the graph of y=f(x) is partly above and partly below thex-axis, the formula given below generates the net area. That is, thearea above the axis minus the area below the axis.

The trapezoidal rule (also known as the trapezoid rule or trapeziumrule) is an approximate technique for calculating the definite integralas follows

${\int_{a}^{b}{{f(x)}{x}}} \cong {\frac{\Delta \; x}{2}*( {{f( {x\; 0} )} + {f({xn})} + {f( {x\; 2} )} + \ldots + {f( {x( {n - 1} )} )}} )}$${{{Where}\mspace{14mu} \Delta \; x} = \frac{( {b - a} )}{n}},{{x\; 0} = a},{{x\; 1} = {a + {\Delta \; x}}},{{x\; 2} = {{a + {2\Delta \; x\mspace{14mu} \ldots \mspace{14mu} {xn}}} = {{a + {n\; \Delta \; x}} = b}}}$

and ‘n’ is the number of equal length subintervals into which the region[a, b] is divided into.

To calculate area relative to the Normal line, instead of x-axis, pixelvalues relative to the selected normal need to be calculated which equalto the actual pixel value−the selected normal value:

If relative pixel value is positive it indicates that the point fallsabove the normal line and if negative it falls below the normal line.Whenever the relative pixel value across the curve goes from positive tonegative or vice versa it is an indication that there has been acrossover across the normal line. The algorithm for computing the areaabove and below the normal line can be summarized as follows:

Calculate relative pixel values

Find out where the crossover points occur

Split the curve into positive and negative regions

Calculate area for each region separately using the Trapezoidal rule

Finally, combine all positive areas to obtain area above normal line andall the negative areas to obtain the area below the normal line

FIG. 23 shows a plot of a sample profile line and a normal line. Asshown in the figure the sample profile lines crosses the normal line atthree points dividing the curve into three regions. Regions 1 and 3 fallabove the normal line and have positive relative pixel values whereasthe region 2 falls below the normal line and has negative relative pixelvalues.

To calculate the area above and below the normal for the sample plot thearea for the three regions need to be calculated individually using theTrapezoidal rule.

${{Area}\mspace{14mu} {for}\mspace{14mu} {the}\mspace{14mu} {regions}\mspace{14mu} 1} = {{\int_{a}^{b\; 1}{f\; 1(x)}} \cong {\frac{\Delta \; x}{2}*( {{f\; 1(a)} + {f\; 1( {b\; 1} )} + {2*( {{f\; 1( {x\; 1} )} + {f\; 1( {x\; 2} )} + \ldots + {f\; 1( {x( {n - 1} )} )}} )}} )}}$

Where f1(x) defines the curve in region 1

${{\Delta \; x} = \frac{( {{b\; 1} - a} )}{n}},{{x\; 1} = {a + {\Delta \; x}}},{{x\; 2} = {{a + {2\Delta \; x\mspace{14mu} \ldots \mspace{14mu} {xn}}} = {{a + {n\; \Delta \; x}} = {b\; 1}}}}$

and ‘n’ is the number of equal length subintervals into which the region[a,b1] is divided into.

${{Area}\mspace{14mu} {for}\mspace{14mu} {region}\mspace{14mu} 2} = {{\int_{b\; 1}^{b\; 2}{f\; 2(x)}} \cong {\frac{\Delta \; x}{2}*( {{f\; 2( {b\; 1} )} + {f\; 2( {b\; 2} )} + {2*( {{f\; 2( {x\; 1} )} + {f\; 2( {x\; 2} )} + \ldots + {f\; 2( {x( {n - 1} )} )}} )}} )}}$

Where f2(x) defines the curve in region 2

${{\Delta \; x} = \frac{( {{b\; 2} - {b\; 1}} )}{n}},{{x\; 1} = {{b\; 1} + {\Delta \; x}}},{{x\; 2} = {{{b\; 1} + {2\Delta \mspace{14mu} \ldots \mspace{14mu} {xn}}} = {{{b\; 1} + {n\; \Delta \; x}} = {b\; 2}}}}$

and ‘n’ is the number of equal length subintervals into which the region[b1, b2] is divided into.

The area for this region would be negative indicating that it fallsbelow the normal line.

Area above the Normal line can be obtained by adding areas under regions1 and 3=

∫_(a) ^(b1) f1(x)+∫_(b2) ^(b) f3(x)

Area below the Normal line=Area under the region 2=∫_(b1) ^(b2)f2(x)

By counting exactly how many number of pixels fall above or below thereference line the percentage of profile line that falls above or belowthe profile line can be calculated as follows:

${{{{Percentage}\mspace{14mu} {of}\mspace{14mu} {profile}\mspace{14mu} {line}\mspace{14mu} {that}\mspace{14mu} {falls}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} = \frac{( {{Number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} )*100}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {across}\mspace{14mu} {the}\mspace{14mu} {profile}\mspace{14mu} {line}}}{Percentage}\mspace{14mu} {of}\mspace{14mu} {profile}\mspace{14mu} {line}\mspace{14mu} {that}\mspace{14mu} {falls}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} = \frac{( {{Number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} )*100}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {across}\mspace{14mu} {the}\mspace{14mu} {profile}\mspace{14mu} {line}}$${{Percentage}\mspace{14mu} {of}\mspace{14mu} {profile}\mspace{14mu} {line}\mspace{14mu} {that}\mspace{14mu} {falls}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} = \frac{( {{Number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} )*100}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {across}\mspace{14mu} {the}\mspace{14mu} {profile}\mspace{14mu} {line}}$

Maximum rise above the reference line gives the maximum positivedifference in the pixel values between the profile line and thereference line. A rise in this value indicates that the temperature forsome of the pixels along the profile line is getting much hotter thanthe reference value and decrease in this value indicates that themaximum difference between the profile line pixel values and thereference line pixel values is decreasing and that the profile line isgetting closer to the reference line.

Similarly Maximum drop below the reference line can be calculated as themaximum negative difference in the pixel values between the profile lineand the reference line. An increase in the maximum drop indicates thatthe pixels on the profile line are colder than the average referencepixel value.

Average rise and average drop can also be used as factors for comparingthe profile lines with the reference line. Formulae for calculatingaverage rise and average drop are as follows:

${{{{Average}\mspace{14mu} {rise}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} = \frac{{Sum}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}}}{Average}\mspace{14mu} {fall}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}} = \frac{{Sum}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {the}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{14mu} {that}\mspace{14mu} {fall}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {reference}\mspace{14mu} {line}}$

Slopes: Calculating slopes for the profile lines gives information abouthow often the temperature varies along the profile line. A slope linecan be drawn on the profile line every time there has been a significantchange in the pixel value (temperature). A positive slope indicates anincrease in temperature and a negative slope indicates a drop in thetemperature. The steepness of the slope lines indicates the amount ofvariation in temperatures. The steeper the lines the larger thevariation is temperatures and the more irregular the profile line is.

An algorithm for calculating slopes and generating slope lines acrossthe profile line can be summarized as follows:

Select a suitable value for slope variance, a value which indicates howmuch of a difference in pixel values between two points on the profileline is considered as a signification change.

Consider the starting point of the profile line as the starting point ofthe first slope line. Starting from this point and by moving along theprofile line calculate the difference between the current pixel valueand the pixel value at the starting location. If the difference isgreater than or equal to the slope variance, the point at which thedifference exceeds the slope variance becomes the end point for theslope line.

Draw a line on the profile line joining these two points.

Slope for this line can be calculated as follows:

If (x1, y1) represents the x and y coordinates of the starting point and(x2, y2) represent the coordinates of the end point of the slope linethen the slope for this line can be calculated as

${Slope} = \frac{( {{y\; 2} - {y\; 1}} )}{( {{x\; 2} - {x\; 1}} )}$

Save this slope value in an array.

Make the end point of the first slope line as the start point for thenext slope line to be generated and repeat step 2 to determine the newend point.

Once the start and end points of the slope lines is established plot theslope line on to the profile line and then calculate and save the slopevalues.

Repeat the process until the end of profile line is reached

FIG. 21 shows a slope line plotted on to the profile line with a slopevariance of 12.

These are some of the factors that can be calculated from the profileline and reference line plots that help define the signature of the areaof interest.

All the activity done by the clinician on the images can be recorded andsaved in a database. The information can be retrieved on a later date tosee which regions were selected as area of interest on that particularday, and to see what changes have occurred and how the results havechanged with time. This novel approach will enable a trained clinicianto better evaluate the area of interest/wound of the skin andsubcutaneous tissue in a standardized and reproducible format.

The benefits related to using this advancement in long-wave infraredthermal imaging spans improvement in potential care, fulfillingregulatory requirements and fiduciary responsibility by reproducible andstandardized documentation and cost savings secondary to the ability ofclinicians to formulate appropriate individualized care plans forprevention, early intervention and treatment of abnormalities of theskin and subcutaneous tissue.

H. Using the Profile Line Plot to Interpret Wounds

Once a profile line is drawn on the image across the area of interest aprofile line plot can be generated using the algorithm outlined above.The plot can then be used to determine where on the profile line a dropor rise in the pixel value (temperature) occurs. The profile line plotcan be made interactive so that when the user clicks on the plot thecorresponding location on the image can be highlighted and hence makingit easier to interpret. The algorithm for implementing this can bebriefly summarized as follows:

1. Generate an interactive plot for profile line using tools likeTelerik.

2. Create a chart item click event for the plot so that when the userclicks on the profile line plot the x and y values of the click pointare recorded.

3. The X axis value at the click point (saved as ‘index’) shows how faraway the point falls from the start point of the profile line. The Yvalue gives the actual pixel value at the point.

4. To locate this point on the profile line drawn, on the image, theactual X and Y coordinates on the image need to be determined. The X andY coordinates of the click point can be obtained as follows:

5. Calculate the length of the profile line using the start and endcoordinates of the profile line.

6. If (XI,YI) represents the coordinates of the starting point of theprofile line on the image and (X2,Y2) represent the end point then thelength can be calculated as

7. length of the line=L=√{square root over ((x2−x1)²+(y2−y1)²)}

8. deltaX=absolute value of (X2−X1); deltaY=absolute value of (Y2−Y1)

9. x_increment=deltaX/L; y_increment=deltaY/L

10. if (x_increment>0 && y_increment<0)

{

index=L−index:

}

11. The X and Y coordinates of the point that represents the click pointcan then be obtained as X=X1+(index*x_increment);Y=Y1+(index*y_increment);

12. Draw a string on the image at the X and Y coordinates from theprevious step to indicate the click point

Similar technique can be used to determine where a point on the imagefalls on the profile line. The algorithm for doing this can be outlinedas follows:

1. Add a Mouse down click event for the image.

2. Note the X and Y coordinates of the point where the user clicked onthe image.

3. Check whether this point falls on the profile line

4. If the point falls on the profile line calculate the distance betweenthe start point of the profile line and the point where the userclicked.

5. This distance indicates how far the point falls on the plot from thestart point of the graph.

6. Draw on the graph to indicate this point.

FIG. 24 shows a profile line drawn on the image of a hand and FIG. 25shows the profile line plot. The X mark on the graph and the imageindicates the user's selected point.

While this invention has been described with respect to at least oneembodiment, the present invention can be further modified within thespirit and scope of this disclosure. This application is thereforeintended to cover any variations, uses, or adaptations of the inventionusing its general principles. Further, this application is intended tocover such departures from the present disclosure as come within knownor customary practice in the art to which this invention pertains andwhich fall within the limits of the appended claims.

1. A method of detecting abnormalities in mammalian skin andsubcutaneous tissue, said method comprising the steps of: providing ainfrared imaging device, said device comprising a first laser emittingdevice and a second laser emitting device, wherein beams from said firstand second laser emitting devices converge at a predetermined distancefrom a target; arranging said imaging device at a distance from saidtarget such that said beams converge; defining a first skin area on amammal; acquiring a long-wave infrared image of said first skin areafrom said predetermined distance using said imaging device; defining apixel value for a predetermined range of temperature in said first skinarea; calculating an average pixel value for said first skin area;defining a second skin area on a mammal; acquiring a long-wave infraredimage of said second skin area from said predetermined distance usingsaid imaging device; defining a pixel value for a predetermined range oftemperature in said second skin area; and generating a histogram of saidpixel values within said skin areas.